import gradio as gr


import torch
import torchvision

model = torch.load('v4-epoch=19-val_loss=0.6964-val_accuracy=0.8964.ckpt', map_location=torch.device('cpu'))
state_dict = checkpoint["state_dict"]                                                                                                                                                                                                         
model_weights = state_dict                                                                                                                                                                                                                    
for key in list(model_weights):                                                                                                                                                                                                               
    model_weights[key.replace("backbone.", "")] = model_weights.pop(key)                                                                                                                                                                      
model.load_state_dict(model_weights).eval()

import requests
from PIL import Image
from torchvision import transforms

# Download human-readable labels for ImageNet.
labels = ['good', 'ill']
CROP=384

def predict(inp):
  img = torchvision.transforms.ToTensor()(inp)
  img = torchvision.transforms.Resize((800, 800))(img)
  img = torchvision.transforms.CenterCrop(CROP)(img)
  img = img.unsqueeze(0)
  with torch.no_grad():
    prediction = model(img).softmax(1).numpy()
    confidences = {labels[i]: float(prediction[i]) for i in range(2)}    
  return confidences

import gradio as gr

gr.Interface(fn=predict, 
             inputs=gr.Image(type="pil"),
             outputs=gr.Label(num_top_classes=1),
             ).launch()